The proposed \(r^i\)  desirably measures the task-related state novelty and the theoretical proof is provided in the work [1]}. By rewarding agents to explore novel states related to the given tasks, agents can obtain surpassing performances without distraction than previous works [2]}, [3]}. However, the importance weight \(\beta \)  that balances between compression and preservation of information has to be carefully designed for different tasks, where an adaptive generalization is more desirable.
